Shotgun Lipidomics Elucidates the Lipidome Alterations of the Mcl-1 Inhibitor S63845 in AML Cell Lines with a Focus on Sphingolipids

Year 2022, , 209 - 214, 31.12.2022

Melis Kartal Yandım , Mesut Bilgin

https://doi.org/10.26650/experimed.1196117

Abstract

Objective: Acute myeloid leukemia (AML) is a vigorous type of leukemia requiring effective treatment. Myeloid cell leukemia-1 (Mcl-1) is an anti-apoptotic molecule that is upregulated in AML and is studied as a target for treatment. The specific Mcl-1 inhibitor, S63845, has antiproliferative effects on AML cells. Bioactive sphingolipids have crucial roles in cells and regulate Mcl-1 stability. This study aimed to elucidate the changes in lipid profiles of AML cell lines in response to Mcl-1 inhibitor S63845 treatment, with a special focus on sphingolipids.

Materials and Methods: The cytotoxic effects of S63845 were identified in the AML cell lines MV4-11, HL60, and KG1 using the MTT cell proliferation assay. Lipidome analysis was conducted by quantitative shotgun lipidomics covering 378 individual lipid species in 26 classes within the major lipid categories.

Results: The IC50 values of S63845 have been calculated as 7 nM for MV4-11, 53 nM for HL60, and 479 nM for KG1. The lipidome results reveal the S63845 treatment to increase ceramide (Cer) levels in the MV4-11 and KG1 cell lines at the expense of downstream sphingolipids while increasing the hexosylceramide (HexCer) levels in the HL60 cell line at the expense of the Cer and sphingomyelin (SM).

Conclusion: This study showed S63845 to be able to suppress cell proliferation by altering lipid compositions in AML cell lines. More importantly, the study suggested S63845 to differentially affect the lipid profiles of AML cell lines.

Keywords

Mcl-1, small molecule inhibitors, S63845, acute myeloid leukemia, shotgun lipidomics, bioactive sphingolipids

Thanks

We thank the technicians at Danish Cancer Society Research Center due to their supports during the experiments. We especially thank Dianna Skousborg Larsen for her assistance during the lipidomics analysis.

References

• 1. Padmakumar D, Chandraprabha VR, Gopinath P, Vimala Devi ART, Anitha GRJ, Sreelatha MM, et al. A concise review on the molecular genetics of acute myeloid leukemia. Vol. 111, Leukemia Research. Elsevier Ltd; 2021. [CrossRef] google scholar
• 2. Newell LF, Cook RJ. Advances in acute myeloid leukemia. Vol. 16. 375, BMJ (Clinical research ed.). NLM (Medline); 2021. p. n2026. [CrossRef] google scholar
• 3. Pearson JM, Tan SF, Sharma A, Annageldiyev C, Fox TE, Abad JL, et al. Ceramide analogue SACLAC modulates sphingolipid levels and 17. MCL-1 splicing to induce apoptosis in acute myeloid leukemia. Mol Cancer Res 2020; 18(3): 352-63. [CrossRef] google scholar
• 4. Kotschy A, Szlavik Z, Murray J, Davidson J, Maragno AL, le Toumelin-Braizat G, et al. The MCL1 inhibitor S63845 is tolerable 18. and effective in diverse cancer models. Nature 2016; 538(7626): 477-82. [CrossRef] google scholar
• 5. Bolomsky A, Vogler M, Köse MC, Heckman CA, Ehx G, Ludwig H, et al. MCL-1 inhibitors, fast-lane development of a new class of anti- 19. cancer agents. J Hematol Oncol 2020;13: 173. [CrossRef] google scholar
• 6. Wang H, Guo M, Wei H, Chen Y. Targeting MCL-1 in cancer: current status and perspectives. J Hematol Oncol 2021; 14(1): 67. [CrossRef] google scholar
• 7. Moujalled DM, Pomilio G, Ghiurau C, Ivey A, Salmon J, Rijal S, et al. Combining BH3-mimetics to target both BCL-2 and MCL1 has 21. potent activity in pre-clinical models of acute myeloid leukemia. Leukemia 2019; 33(4): 905-17. [CrossRef] google scholar
• 8. Malyukova A, Ujvari D, Yektaei-Karin E, Zovko A, Madapura HS, Keszei M, et al. Combination of tyrosine kinase inhibitors and the MCL1 inhibitor S63845 exerts synergistic antitumorigenic effects 22. on CML cells. Cell Death Dis 2021; 12(10). [CrossRef] google scholar
• 9. Stefanko A, Thiede C, Ehninger G, Simons K, Grzybek M. Lipidomic approach for stratification of acute myeloid leukemia patients. 23. PLoS One 2017; 12(2). [CrossRef] google scholar
• 10. Ogretmen B, Hannun YA. Biologically active sphingolipids in cancer pathogenesis and treatment. Nat Rev Cancer 2004; 4(8): 604-16. [CrossRef] 24. google scholar
• 11. Powell JA, Lewis AC, Zhu W, Toubia J, Pitman MR, WallingtonBeddoe CT, et al. Targeting sphingosine kinase 1 induces MCL1-dependent cell death in acute myeloid leukemia. Blood 2017; 6: 771-82. [CrossRef] google scholar
• 12. Tan SF, Liu X, Fox TE, Barth BM, Sharma A, Turner SD, et al. Acid ceramidase is upregulated in AML and represents a novel 26. therapeutic target. Oncotarget 2016; 7(50): 83208-22. [CrossRef] google scholar
• 13. Lewis AC, Wallington-Beddoe CT, Powell JA, Pitson SM. Targeting sphingolipid metabolism as an approach for combination therapies in haematological malignancies. Cell Death Discov 2018; 4(1): 72. [CrossRef] 27. google scholar
• 14. Kozanoglu I, Yandim MK, Cincin ZB, Ozdogu H, Cakmakoglu B, Baran Y. New indication for therapeutic potential of an old well- 28. known drug (propranolol) for multiple myeloma. J Cancer Res Clin Oncol 2013; 139(2): 327-35. [CrossRef] google scholar
• 15. Bligh EG, Dyer WJ. A rapid method of total lipid extraction and purification. Can J Biochem Physiol 1959; 37(8): 911-7. [CrossRef] google scholar
• 16. Almeida R, Pauling JK, Sokol E, Hannibal-Bach HK, Ejsing CS. Comprehensive Lipidome Analysis by Shotgun Lipidomics on a Hybrid Quadrupole-Orbitrap-Linear Ion Trap Mass Spectrometer. J Am Soc Mass Spectrom 2015; 26(1): 133-48. [CrossRef] google scholar
• 17. Nielsen I0, Groth-Pedersen L, Dicroce-Giacobini J, Jonassen ASH, Mortensen M, Bilgin M, et al. Cationic amphiphilic drugs induce elevation in lysoglycerophospholipid levels and cell death in leukemia cells. Metabolomics 2020; 16(9): 91. [CrossRef] google scholar
• 18. Ejsing CS, Sampaio JL, Surendranath V, Duchoslav E, Ekroos K, Klemm RW, et al. Global analysis of the yeast lipidome by quantitative shotgun mass spectrometry. Proc Natl Acad Sci USA 2009; 106(7): 2136-41. [CrossRef] google scholar
• 19. Klose C, Surma MA, Gerl MJ, Meyenhofer F, Shevchenko A, Simons K. Flexibility of a eukaryotic lipidome--insights from yeast lipidomics. PLoS One 2012; 7(4): e35063. [CrossRef] google scholar
• 20. Sampaio JL, Gerl MJ, Klose C, Ejsing CS, Beug H, Simons K, et al. Membrane lipidome of an epithelial cell line. Proc Natl Acad Sci USA 2011; 108(5): 1903-7. [CrossRef] google scholar
• 21. Ejsing CS, Moehring T, Bahr U, Duchoslav E, Karas M, Simons K, et al. Collision-induced dissociation pathways of yeast sphingolipids and their molecular profiling in total lipid extracts: a study by quadrupole TOF and linear ion trap-orbitrap mass spectrometry. J Mass Spectrom 2006; 41(3): 372-89. [CrossRef] google scholar
• 22. Herzog R, Schuhmann K, Schwudke D, Sampaio JL, Bornstein SR, Schroeder M, et al. LipidXplorer: A Software for Consensual CrossPlatform Lipidomics. PLoS One 2012; 7(1): e29851. [CrossRef] google scholar
• 23. Herzog R, Schwudke D, Shevchenko A. LipidXplorer: Software for Quantitative Shotgun Lipidomics Compatible with Multiple Mass Spectrometry Platforms. Curr Protoc Bioinformatics 2013; 43(1). [CrossRef] google scholar
• 24. Ewald L, Dittmann J, Vogler M, Fulda S. Side-by-side comparison of BH3-mimetics identifies MCL-1 as a key therapeutic target in AML. Cell Death Dis 2019; 10(12). [CrossRef] google scholar
• 25. Wei AH, Roberts AW, Spencer A, Rosenberg AS, Siegel D, Walter RB, et al. Targeting MCL-1 in hematologic malignancies: Rationale and progress. Blood Rev 2020; 44: 100672. [CrossRef] google scholar
• 26. Escriba P v., Busquets X, Inokuchi J ichi, Balogh G, Török Z, Horvath I, et al. Membrane lipid therapy: Modulation of the cell membrane composition and structure as a molecular base for drug discovery and new disease treatment. Prog Lipid Res 2015; 59: 38-53. [CrossRef] google scholar
• 27. Ogretmen B. Sphingolipid metabolism in cancer signalling and therapy. Nat Rev Cancer 2018; 18(1): 33-50. [CrossRef] google scholar
• 28. Pal P, Atilla-Gokcumen GE, Frasor J. Emerging Roles of Ceramides in Breast Cancer Biology and Therapy. Int J Mol Sci 2022; 23(19): 11178. [CrossRef] google scholar